DATA ANALYSIS AND FINDINGS
5.2.8 Common Method Bias
It is widely acknowledged that common method variance may pose a serious threat to the studies whose data is collected from a single respondent or through a single method (e.g., Podsakoff et al., 2003). Common method variance can also contribute to measurement error which may affect structural parameter estimates and the statistical significance of hypothesis testing (Podsakoff et al., 2003). To
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reduce the potential problems attributable to common method variance, several approaches were adopted in this study.
First, this study employed a multiple informant design which comprised one senior executive, one R&D manager, one marketing manager, seven employees, and five customers. The use of multiple informants can help rule out or minimise common method bias (e.g., Liao & Chuang, 2007; Zhou et al., 2008; Liao & Subramony, 2008; Morgan et al., 2009). This approach ensured data for independent variables and dependent variables were obtained from different sources – an approach recommended by scholars to reduce the threat of common method bias (Podsakoff et al., 2003; Atuahene-Gima, 2005).
Second, following the recommendations of scholars such as Podsakoff et al. (2003), different scale instructions, endpoints, and formats were used for the focal constructs in order to reduce the potential bias that is commonly associated with the use of the same scale poles and anchors throughout the survey.
Third, following Verhoef and Leeflang (2009) a factor analysis was conducted. A factor analysis of all items resulted in eight factors which accounted for a total variance of 77.9%, with the first factor accounting for 28%. The results indicate that the largest first factor did not account for the majority of the total variance explained, thus indicating that common method variance is not a major issue.
Finally, since interaction and mediation effects represent the majority of the hypotheses, the model overall is less likely to suffer from potential bias. In fact, analytical derivations and simulation studies demonstrated that common method bias reduces the probability to find significant interaction and mediation effects (Evans, 1985; Siemsen et al., 2010).
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5.3 HYPOTHESES TESTING
After having determined that the construct measures used in this study were psychometrically sound, emphasis now moves to empirically testing the hypotheses presented in Chapter Three. Following the theoretical structure discussed in Chapter Three, and consistent with previous studies, this study adopted the variance partitioning procedures recommended by Jaccard et al. (1990), and used individual hierarchical moderated regression analyses, undertaken in steps.
Following Zhou’s et al. (2008) approach, during data analysis, this study included firm age, size, and ownership as controls. Firm age was measured by the logarithm of the number of years the firm has been in operation and firm size was measured by the logarithm of number of employees. This study coded the firm product brand ownership as a dummy variable: 1 = domestic brand and 0 = foreign-own brand. Because multicollinearity can cause a problem when analysing moderating effects, the predictor variable and the moderator variable were mean-centered to reduce any potential multicollinearity (Aiken & West, 1991).
Hypothesis 1
Hypothesis 1 stated that brand equity mediates the relationship between product innovation capability and the firms’ ability to achieve both customer value creation and firm value appropriation. To test for mediation, this study adopted the approach suggested by James et al. (2006), Siren et al. (2012), and Hayes (2013), in which a direct relationship between the independent variable and
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dependent variable is not expected. Therefore, full mediation is supported if (1) there is a non-significant relationship between the independent variable and dependent variable; (2) there is a significant relationship between independent variable and the mediator variable; and (3) there is a significant relationship between the mediator variable and the dependent variable.
As shown in Table 5.10, Model 1 (step 2), the independent variable (product innovation capability) is not significantly related to the dependent variable (customer value creation * firm value appropriation) (b =.08, p>.10), thus meeting the first requirement. In addition, as also shown in Table 5.10, Model 2 (step 2), the independent variable (product innovation capability) is significantly related to the mediator (brand equity) (b=.49, p<.01), thus meeting the second requirement. Furthermore, as shown in Table 5.11 (step 2), the mediator (brand equity) is significantly related to the dependent variable (customer value creation * firm value appropriation) (b =.19, p<.05), thus meeting the third requirement. These results support full mediation of the relationship between product innovation capability and the dual outcomes of customer value creation and firm value appropriation through brand equity.
Table 5.10:The Effects of PIC on BE and CVC*FVA
Variable
Model 1: CVC*FVA Model 2: BE
Step 1 Step 2 Step 1 Step 2
Firm size .11 (1.28) .08 (0.98) .21** (2.60) .08 (1.16) Firm age .12 (1.33) .12 (1.36) .09 (1.16) .08 (1.13) Ownership .13 (1.46) .11 (1.33) .26** (3.18) .20** (2.84) Product innovation capability (PIC) --- .08 (0.92) --- .49*** (6.50) R-square .04 .05 .13 .35 Adjusted R-square .02 .03 .11 .33
***p<.01, **p<.05, *p<.10 (one-tailed test for hypothesised relationships; two-tailed test for controls).
131 Hypothesis 2
Hypothesis 2 stated the interaction between market orientation and brand orientation is positively related to the firm’s product innovation capability. As shown in Table 5.12, step 3, this hypothesis is supported with the effect size of .19 and significant level is lower than .05 (b=.19, p<.05).
Table 5.11:The Effects of BE on CVC*FVA
Variable CVC*FVA Step 1 Step 2 Firm size .11 (1.28) .07 (0.80) Firm age .12 (1.33) .13 (1.55) Ownership .13 (1.46) .08 (0.87)
Brand equity (BE) --- .19** (2.02)
R-square .04 .07
Adjusted R-square .02 .05
***p<.01, **p<.05, *p<.10 (one-tailed test for hypothesised relationships; two-tailed test for controls).
Note: CVC = Customer Value Creation; FVA = Firm Value Appropriation
Table 5.12:The Effects of MO*BO on Product Innovation Capability
Variable
Product innovation capability
Step 1 Step 2 Step 3
Firm size .26** (3.10) .12 (1.64) .14* (1.84)
Firm age .03 (0.35) .07 (0.96) .07 (0.97)
Ownership .11 (1.39) .08 (0.97) .03 (0.35)
Market orientation (MO) --- .33** (3.22) .30** (2.84) Brand orientation (BO) --- .26** (2.55) .18* (1.73)
MO*BO --- --- .19** (2.18)
R-square .08 .33 .36
Adjusted R-square .06 .30 .33
***p<.01, **p<.05, *p<.10 (one-tailed test for hypothesised relationships; two-tailed test for controls).
132 Hypothesis 3
Hypothesis 3 stated that the relationship between the interaction of market orientation and brand orientation and the dual outcomes of customer value creation and firm value appropriation is mediated by product innovation capability and brand equity. Following the approach of Gong et al. (2012), to test this hypothesis this study adopted the bootstrapping procedures recommended by Preacher and Hayes (2008).
Table 5.13:Results of Mediation (MO*BO – Product Innovation Capability – Brand Equity – CVC*FVA)
Indirect effect(s) of MO*BO on CVC*FVA
Effect Boot SE BootLLCI BootULCI
Total: .0502 .0345 -.0151 .1266
Indirect 1 .0035 .0305 -.0497 .0728
Indirect 2 .0242 .0173 .0003 .0758
Indirect 3 .0225 .0195 -.0042 .0739
Indirect effect key
Indirect 1 MO*BO Product innovation CVC*FVA
Indirect 2 MO*BO Product innovation Brand equity VCV*FVA Indirect 3 MO*BO Brand equity VCV*FVA
Level of confidence for all confidence intervals in output: 95.00
Note: BootLLCI = Lower confident interval; BootULCI = Upper confident interval
As shown in Table 5.13 (indirect 1), the indirect effect from MO*BO to CVC*FVA through product innovation capability is non-significant (LLCI = - .0497; ULCI = .0728, confidence level = 95%) because the changes between LLCI and ULCI contain zero. Further, the indirect effect from MO*BO to CVC*FVA through brand equity is also non-significant (indirect 3) (LLCI = - .0042; ULCI = .0739, confidence level = 95%) because the changes between LLCI and ULCI also contain zero. However, the indirect effect from MO*BO to
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CVC*FVA through product innovation capability and then through brand equity (indirect 2) is significant (LLCI = .0003, ULCI = .0758, confidence level = 95%) because the changes between LLCI and ULCI does not contain zero. Thus, hypothesis 3 is supported. These results indicate that neither product innovation capability nor brand equity mediates the relationship between MO*BO and the dual outcomes. The relationship between MO*BO and CVC*FVA is mediated through product innovation capability and then through brand equity.
To further support whether the relationship between the interaction of market orientation and brand orientation and the dual outcomes of customer value creation and firm value appropriation can be mediated by brand equity and then product innovation capability, this study adopted the bootstrapping procedures recommended by Preacher and Hayes (2008).
As shown in Table 5.14, all the indirect effects (indirect 1, indirect 2, and indirect 3) are non-significant because the changes between LLCI and ULCI within the three indirect effects contain zero. These results indicate that the link between the interaction of market orientation and brand orientation and the dual outcomes of customer value creation and firm value appropriation cannot be connected through brand equity and then product innovation capability, further confirming hypothesis 3.
134 Table 5.14: Results of Mediation Test through Bootstrapping (MO*BO – Brand Equity – Product Innovation Capability – CVC*FVA)
Indirect effect(s) of MO*BO on CVC*FVA
Effect Boot SE BootLLCI BootULCI
Total: .0502 .0345 -.0151 .1266
Indirect 1 .0467 .0301 -.0018 .1218
Indirect 2 .0015 .0136 -.0206 .0365
Indirect 3 .0019 .0181 -.0284 .0466
Indirect effect key
Indirect 1 MO*BO Brand equity CVC*FVA
Indirect 2 MO*BO Brand equity Product innovation VCV*FVA Indirect 3 MO*BO Product innovation VCV*FVA
Level of confidence for all confidence intervals in output: 95.00
Hypothesis 4
Hypothesis 4 stated that transformational leadership moderates the product innovation capability – brand equity relationship. As shown in Table 5.15, Step 3, this relationship is supported with the effect size of .24 and the significant level is lower than .05 (b=.24, p<.05).
Table 5.15:The Moderating Effect of TFL on Product Innovation Capability – Brand Equity Relationship
Variable
Brand equity
Step 1 Step 2 Step 3
Firm size .21** (2.60) .09 (1.20) .08 (1.13)
Firm age .09 (1.16) .07 (1.03) .07 (1.05)
Ownership .26** (3.18) .21** (2.88) .20** (2.85)
Product innovation capability (PIC) --- .48*** (6.45) .29** (2.23) Transformational leadership (TFL) --- .15* (1.88) .19** (1.98)
PIC*TFL --- .24** (2.12)
R-square .13 .32 .38
Adjusted R-square .11 .29 .35
***p<.01, **p<.05, *p<.10 (one-tailed test for hypothesised relationships; two-tailed test for controls).
135 5.4 SUMMARY OF RESULTS
As shown in Table 5.16, the findings indicate that all hypotheses are supported.
Table 5.16: Results of Hypotheses Testing
No Hypothesis Result
H1 Brand equity mediates the relationship between product innovation capability and the dual outcomes of customer value creation and firm value appropriation.
Supported
H2 The interaction between market orientation and brand orientation is
positively related to the firm’s product innovation capability. Supported
H3
The relationship between the interaction of market orientation and brand orientation and the dual outcomes of customer value creation and firm value appropriation is mediated by product innovation and brand equity in sequence.
Supported
H4 Transformational leadership moderates the product innovation
capability – brand equity relationship. Supported
5.5 CONCLUSION
Following the research design and methodology discussed and presented in Chapter Four, empirical data were collected for addressing the research questions discussed and presented in Chapter One and hypotheses discussed and presented in Chapter Three. In this Chapter, the data collected were analysed and the findings were presented. This Chapter also presented the preliminary assessment of sample composition, the descriptive statistics of all items, assessments of non-response bias and common method variance. Then, measurement reliability and validity were examined (i.e. factor loadings, composite reliability, average variance extracted, discriminant and convergent validity). Once the adequacy of the measurement model pertaining to the proposed constructs was affirmed, this study examined the hypotheses using
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individual hierarchical moderation regression analyses conducted in steps and the SPSS Macro recommended by Preacher and Hayes (2008). The results presented in this chapter will be used for the development of insightful discussion and implications in the next chapter (Chapter Six).
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